from keras.datasets import mnist
from keras import models
from keras import layers
from keras.utils import to_categorical
# import matplotlib.pyplot as plt

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# digit = train_images[1]
# print(digit)
# plt.imshow(digit, cmap=plt.cm.binary)
# plt.show()

#
train_images = train_images.reshape((60000, 28 * 28))
train_images = train_images.astype('float32') / 255

test_images = test_images.reshape((10000, 28 * 28))
test_images = test_images.astype('float32') / 255
#
network = models.Sequential()
network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))
network.add(layers.Dense(10, activation='softmax'))
network.compile(optimizer='rmsprop',
                loss='categorical_crossentropy',
                metrics=['accuracy'])



train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

network.fit(train_images, train_labels, epochs=5, batch_size=128)
